bugfix

nnet/xvector.py

@@ -520,7 +520,7 @@ class Xtractor(torch.nn.Module):
             self.sequence_network = PreHalfResNet34()
             self.embedding_size = 512
 
-            self.before_speaker_embedding = torch.nn.Linear(in_features = 2560,
+            self.before_speaker_embedding = torch.nn.Linear(in_features = 5120,
                                                             out_features = self.embedding_size)
 
             self.stat_pooling = AttentivePooling(256, 80)
@@ -1110,8 +1110,8 @@ def xtrain(speaker_number,
     non_indices = torch.tril(~mask, -1).numpy()
     tar_non_ratio = numpy.sum(tar_indices)/numpy.sum(non_indices)
     non_indices *= numpy.random.choice([False, True], size=non_indices.shape, p=[1-tar_non_ratio, tar_non_ratio])
-    tar_indices *= numpy.random.choice([False, True], size=tar_indices.shape, p=[0.9, 0.1])
-    non_indices *= numpy.random.choice([False, True], size=non_indices.shape, p=[0.9, 0.1])
+    #tar_indices *= numpy.random.choice([False, True], size=tar_indices.shape, p=[0.9, 0.1])
+    #non_indices *= numpy.random.choice([False, True], size=non_indices.shape, p=[0.9, 0.1])
 
     logging.critical("val tar count : {:d}, non count : {:d}".format(numpy.sum(tar_indices), numpy.sum(non_indices)))